Tackling biases in clinical trials to ensure diverse representation and effective outcomes

Professor Sabine Oertelt-Prigione has been working in the field of sex and gender-sensitive research for the last 15 years. Her current work is focused on trying to understand how sex and gender-sensitive medicine can be successfully implemented in research and practice as well as methods to investigate gender in medical research. Dr. Brandon Turner is a resident physician in the Department of Radiation Oncology at Massachusetts General Hospital and Brigham and Women’s Hospital. He has conducted and is involved in numerous studies looking to evaluate race and ethnicity reporting and representation in clinical trials. In this interview for Nature Communications, Sabine Oertelt-Prigione, and Brandon Turner share their knowledge about the biases that can occur in clinical trials and how they can be minimized.

1.The reliability of the results of a randomized trial depends on the extent to which potential sources of bias have been avoided.Could you please start with the definition and classification of bias in interventional clinical trials?Sabine Oertelt-Prigione-If we consider a two-arm trial comparing an intervention with a control group as an example, there are a few examples of bias.Selection bias, which occurs when individuals with specific characteristics, e.g., comorbidities, are assigned more frequently to one of the two arms of the trial.One could then attribute the trial outcome to the efficacy of the intervention, although the individuals in the control group were simply more ill to begin with.A second bias is information bias or classification bias, where insufficient or inadequate data is recorded; again, leading to potential misclassification of the trial findings.Performance bias occurs when there are systematic differences, for example in how the intervention under study is being performed.If we compare a contraceptive pill that must be swallowed with an intervention that requires some experience, e.g., the use of a female condom, results could be biased not due to differences in effectiveness, but differences in performance of the intervention.Another bias is detection bias, which is associated with differences in how the outcomes under study are being determined.In fact, certain expectations by the study participant or the trialist might lead to heightened attention to certain outcomes that are in line with the original expectations impacting the likelihood of detection.Attrition bias occurs when participants abandon the two groups at different rates due to unidentified factors.Confounding bias, on the other hand, occurs when the trial effects are attributed to an intervention, although they are caused by an unidentified factor associated with exposure and outcome.A last and important form of bias is reporting bias, which is linked to how data is presented and reported in publications.In our current system authors are rewarded for novelty and positive findings, leading to a potential underreporting of negative or confirmative results.Brandon Turner-As highlighted by Sabine, there are so many sources of potential bias within a clinical trial.I find it helpful to structure them along two dimensions.The first dimension is whether the bias impacts the internal validity (i.e., does the trial measure what they purport to measure) or external validity (i.e., can the trial results be applied to patients in the real world) of the study.Most of the biases we discuss impact the internal validity of a study, however reporting bias primarily impacts external validity.The second dimension is where the bias emerges within the lifecycle of a trial.At the study design stage, biases that can emerge include selection bias (e.g., poor randomization or skewed inclusion/exclusion criteria), confounding bias (e.g., not measuring or stratifying by potential confounders), or subtle biases due to flawed treatment arms (e.g., using a weak or flawed comparison arm to demonstrate the efficacy of the experimental intervention).At the trial conduct phase, differences between providers or study facilities in how data is collected or recorded (possible sources of classification bias or detection bias) or in how interventions are administered (possible source of performance bias) can produce bias.This also includes differences between treatment arms (e.g., attrition bias).Finally, at the data analysis and reporting stage, biases in analysis (e.g., use of suboptimal endpoints or not controlling for confounders) or reporting bias (e.g., selective reporting of specific results) can emerge.Additionally, while it's not a form of bias within a specific trial, there can be bias in the wider clinical research enterprise in terms of which diseases and which therapies get funding for a clinical trial at all.
2. What steps are conventionally taken to avoid such biases?Are there firm regulations or more suggested guidelines on how to avoid bias and are such regulations/ guidelines country-and/or context-specific?Sabine Oertelt-Prigione-Several strategies have been developed to avoid most of these biases and are now common practice when executing pharmacological and intervention trials.One measure is randomization, which is the process of randomly assigning participants to one of the-two or more-groups under study to avoid selection bias.A second strategy against selection and detection bias is blinding.This means that neither the participants nor the investigators know which trial arm the person has been assigned to.Furthermore, trials should follow an intentionto-treat design, meaning that individuals will be assigned to one of the two groups and that nature communications (2024) 15:1407 | 1 their information will be analyzed as part of that group regardless of the outcome.This should maintain the benefits of randomization and reduce selection, detection, and attrition bias.Last, all trials should be officially registered in one of the national or international repositories available and ideally, detailed trial protocols should be published.After trial completion, publication of positive as well as negative results should be ensured to avoid publication bias.
Regarding regulations, there are both national and international guidelines on how to conduct trials that detail which requirements need to be fulfilled.While many requirements are binding, the inclusion of women and men as well as racial or ethnic minorities in clinical trials is not standardized.to include gender.The majority of participants in currently performed trials and surveys identify as men or women, and if the trial population is relatively small, statistical challenges might emerge when a limited number of participants identify as another gender.
To guarantee statistical power and allow the detection of meaningful associations, a selective oversampling of certain gender identities could be considered upon trial recruitment, although this is still rarely done.
Researchers should be aware that enquiring about sex and gender during a trial will be relevant to answering different questions or even different angles of the same questions.
For example, if I want to investigate differences in side effects, I might have to know about both sex and gender.In fact, biological differences in enzymatic metabolism might be associated with sex differences, however, differences in reporting of side effects and being listened to might have a strong gender component.

How are study participants of different races and ethnicities represented in current clinical trials?
Brandon Turner-There are multiple lenses through which we can look at representation.Often when we talk about a patient's race and ethnicity, we're blending two concepts.The first is their genetic heritage, which may tell you something about the way patients from a particular group may react differently to a disease or intervention as a result of commonly inherited genes.The second is their sociocultural context, which brings in all the behavioral, cultural, and environmental factors that we also know impact people's health.The latter is more directly aligned with the social construct of race and ethnicity.We very poorly understand representation by genetic heritage right now, mostly because of the cost to acquire this information, but no doubt there also would be ethical and privacy hurdles.
Globally, there has been a major shift with the emergence of clinical research emerging from East Asia, particularly China.This boosts Asian representation in trials.For example, the recent PD-1 inhibitor Sintilimab (Eli Lily) was developed using enrolment entirely from Chinese facilities.In contrast, its major predecessor, Nivolumb (Bristol-Myers Squibb Company) was developed using enrolment across 14 different countries in North America, South America, Europe, and Asia.African involvement in clinical trials has been minimal comparatively, which limits a potential source of data from patients with African ancestry.However, most of the research on demographic representation has emerged from the US, which also runs the most clinical trials currently.There, recent data suggests that all minority groups are underrepresented relative to their population.When comparing to the US population, the data suggests the largest disparities may in fact be within Asian American and Latino-American communities.However, when disease burden and epidemiology are taken into consideration, the largest disparities are seen in Black Americans.This relationship can be further nuanced when you consider geographic distribution and socioeconomic status.
5. What are the consequences of sex and gender bias in clinical trials?Sabine Oertelt-Prigione-In my opinion, sexand gender-sensitive medicine is about three core principles: equity, safety, and change.
Equity in terms of offering the best possible access and care to all individuals regardless of their sex, gender, or any other individual characteristics.Safety in the form of respect and acceptance as well as the availability of robust information about the effectiveness of a therapy and the absence of side effects.And change, because the achievement of the former two core principles will require systemic changes in the way we currently practice medicine.
Sex and gender bias substantially threatens the safety of therapy, as a biased study will limit the availability of reliable information about effectiveness and potential side effects for all participants.This can be due to inadequate recruitment, lack of statistical power at the subgroup level, or reporting bias.It is well documented that female patients report more side effects for most drugs, ranging from cardiovascular medications to chemotherapy.We still have very little information about the impact of side effects in gender minorities, although this might be an important area of study for example for individuals taking genderaffirming hormones that might interact with other medications.Furthermore, safety is not solely a question of pharmacodynamics but also of health equity.Barriers to access to care, such as economic constraints and various forms of discrimination, can prevent groups of individuals from obtaining the care they need and deserve.As mentioned before, clinical trials mostly focus on sex, yet ignoring gender can be a significant source of bias, especially upon recruitment.People might not feel appropriately addressed by our currently developed gender-neutral recruitment and information materials or might not feel sufficiently reassured when invited to participate.Furthermore, being a gender minority can be a source of exclusion from trials for several reasons.For example, individuals might not be willing to engage with the healthcare system due to prior experiences with discrimination and lack of safety, or trialists might be actively excluding them due to statistical concerns.
The Snapshot project by the FDA (https://www.fda.gov/drugs/drug-approvals-and-databases/ drug-trials-snapshots) offers a user-friendly overview of, among other things, of sex differences in efficacy and side effects.Most listed pharmaceuticals appear to demonstrate no sex differences in the incidence of side effects.Nevertheless, as the agency itself cautions, robust conclusions cannot always be made.This can be due to differences in the recruitment of female and male participants or to the insufficient size of the sex-specific subgroups for robust statistical testing if the expected differences are small.
6. What are the consequences of race and ethnic bias in clinical trials?Brandon Turner-I believe there is an equity dimension and a scientific dimension here.
From an equity perspective, clinical trials have some intrinsic value in themselves.They offer access to the latest therapies and often patients receive extra diligent care, which is partially why trial outcomes can sometimes be rosier than seen in the "real world".A fair clinical research apparatus should endeavor to provide access to this limited resource as evenly as possible except where the science dictates otherwise.Thus, bias away from equal representation in trials denies equitable access to the intrinsic benefits of clinical trial participation.This consequence is intuitive to most observers.From a scientific perspective, poor race and ethnic representation means there may be insufficient power to detect salient differences between demographic groups in terms of an intervention's efficacy or safety.This risk is especially important in the precision medicine era where we are increasingly reliant on exploiting small molecular differences to achieve therapeutic or diagnostic benefits.Interestingly, this risk is often less intuitive to clinicians and scientists precisely because there has historically not been enough research with minority populations which could consequently enable one to observe these subtle differences in the first place.Today there is a growing body of evidence that demonstrate the direct impact of poor representation of race and ethnic minorities on findings ranging from the efficacy of biomarkers for the efficacy of immune checkpoint inhibitors to the likelihood of off-target effects with CRISPR gene editing.In community-based cluster trials, households or entire communities are treated as units of investigation, rather than a single participant.
In this case, the randomization process can be challenging due to unplanned sharing of information between the intervention and control group, or because randomization units might not accept the randomization process.Furthermore, the researched units will be limited in number and thus pose statistical challenges, as the sample size may be too small.When performing a communitybased pharmacological trial, procedural accountability has to be maintained and data safety guaranteed, both of which be challenging if multiple providers are involved.Brandon Turner-Community-based trials bring a new set of challenges.Before even considering participant recruitment, many community practices simply lack the clinician experience or the equipment and resources necessary to participate in increasingly complex modern trials.Training personnel (e.g., completing regulatory paperwork, discussing trial opportunities with patients, properly delivering investigational drugs, and obtaining biospecimens) and acquiring the necessary equipment (e.g., refrigeration and storage for biospecimens and investigational products, specialized devices required for measurements or treatment delivery, etc) require additional startup investment from trial sponsors, though many of these costs become significantly reduced once the site is established.Coordinating centers that assist with training and integrating information from multi-site trials have grown within both public and private institutions.However, data governance at community sites is an additional challenge as data must be either securely stored (which requires additional Q&A nature communications (2024) 15:1407 | equipment) or transferred to a main site, all while ensuring patient privacy and preventing unauthorized access.Digital platforms are emerging to address some of these concerns, though the market is still quite nascent.The primary concern for most sponsors is ensuring the integrity of findings.Meaningful effect sizes can be obscured if measurement precision deteriorates.This may be a challenge for designs that rely on patient selfassessments (whether subjectively or by operating supplied equipment like wearable biometrics) or remote clinician evaluations.Even evaluations by in-person clinicians in the community who are less experienced could potentially exhibit greater inter-rater variability than would be seen in a centralized design at a large health center.These challenges extend also to the monitoring of potential adverse effects and have drawn oversight concern and attention that the focus on providing convenience and access does not come at the expense of vigilance and patient safety.
10. Can you elaborate on recent success stories that we should consider as stepping stones to inclusive trials?Sabine Oertelt-Prigione-When looking at examples about gender, some government agencies have stepped up and developed excellent materials.For example, the division of AIDS (Acquired Immunodeficiency Syndrome) at the National Institutes of Health (NIH) has developed very helpful resources for inclusive gender-sensitive trial design, ranging from communication to analysis.These are good examples when working with a very diverse participant population.In the field of gender-sensitive prevention, the British example of the Football Fans in Training (FFT) trial is also an excellent example of the consideration of masculinities in developing lifestyle interventions.The authors spent many years investigating the underlying concepts of masculinity that led to unhealthy behaviors and identified gender-specific barriers to participation.Based on these findings, they designed a very successful communitybased trial, which set an example for other initiatives in Europe.In the cardiovascular field, we have also seen important progress in the last decade in terms of the inclusion of female participants in clinical trials.Recent analyses have highlighted that the inclusion rates for most conditions are representative of the prevalence in the general population.Funding agencies can also increase support for cost-related burdens as discussed above.However, there is a dearth of knowledge on best practices that can ensure optimal allocation of limited funding resources.There needs to be funding for analytical and implementation studies that evaluate the effectiveness of various strategies for measuring and achieving representative clinical studies.This is crucial, as there remain many uncertainties shared across stakeholders (including regulatory, industry, and academic) that risk creating arbitrary and potentially wasteful goals (e.g., how much representation is "enough", which epidemiological or published data is the appropriate standard for evaluating disease burden, which factors are causal drivers of non-representative trials and what are their relative magnitudes of effect relative to other modifiable factors, etc).
There is a strong history of publishers' role in improving clinical trial transparency and quality.In 2005 when the International Committee of Medical Journal Editors (ICMJE) required clinical trials published in member journals to have registered their trial in a clinical trial registry (such as ClinicalTrials.gov)before patient enrolment, it helped to topple common practices that frequently did not disclose details about trials' design or existence to the public.Publishers should require and help to standardize the reporting of clinical trial patient demographics (which is often nonstandard or missing entirely from publications) including cross-tabulation for the intersection of race, ethnicity, and sex.Analyzing results stratified by race is not feasible for many initial studies given the available power.However, collecting and publishing this information (e.g., in supplements) provides the opportunity for pooled secondary analyses.The effort to enroll underrepresented participants is squandered if their data is not accessible.Publishers also should encourage and promote high-quality and innovative studies that aid our understanding and improvement of trial enrolment dynamics.

Open
Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.© Springer Nature Limited 2024